{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rIkJRvx4MHmrW+WSb6QIyEuR3zaNoq+788v0KNEnfcqAqyJKvlmYhsuZZ8tXP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\nStpddpZmSVos6XeS9tTeHw+VnakZkmZJelHSX2q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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ff7ba504e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 载入数据\n",
    "data = np.genfromtxt(\"LR-testSet.csv\", delimiter=\",\")\n",
    "x_data = data[:,:-1]\n",
    "y_data = data[:,-1]\n",
    "\n",
    "plt.scatter(x_data[:,0],x_data[:,1],c=y_data) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建决策树模型\n",
    "model = tree.DecisionTreeClassifier()\n",
    "# 输入数据建立模型\n",
    "model.fit(x_data, y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 导出决策树\n",
    "import graphviz # http://www.graphviz.org/\n",
    "\n",
    "dot_data = tree.export_graphviz(model, \n",
    "                                out_file = None, \n",
    "                                feature_names = ['x','y'],\n",
    "                                class_names = ['label0','label1'],\n",
    "                                filled = True,\n",
    "                                rounded = True,\n",
    "                                special_characters = True)\n",
    "graph = graphviz.Source(dot_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<g id=\"node10\" class=\"node\"><title>9</title>\r\n",
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       "<!-- 7&#45;&gt;9 -->\r\n",
       "<g id=\"edge9\" class=\"edge\"><title>7&#45;&gt;9</title>\r\n",
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       "<g id=\"node11\" class=\"node\"><title>10</title>\r\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M397,-68C397,-68 317,-68 317,-68 311,-68 305,-62 305,-56 305,-56 305,-12 305,-12 305,-6 311,-0 317,-0 317,-0 397,-0 397,-0 403,-0 409,-6 409,-12 409,-12 409,-56 409,-56 409,-62 403,-68 397,-68\"/>\r\n",
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       "<text text-anchor=\"start\" x=\"316.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 0]</text>\r\n",
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       "</g>\r\n",
       "<!-- 9&#45;&gt;10 -->\r\n",
       "<g id=\"edge10\" class=\"edge\"><title>9&#45;&gt;10</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M395.286,-103.726C390.459,-95.0615 385.353,-85.8962 380.498,-77.1802\"/>\r\n",
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       "<!-- 11 -->\r\n",
       "<g id=\"node12\" class=\"node\"><title>11</title>\r\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M519,-68C519,-68 439,-68 439,-68 433,-68 427,-62 427,-56 427,-56 427,-12 427,-12 427,-6 433,-0 439,-0 439,-0 519,-0 519,-0 525,-0 531,-6 531,-12 531,-12 531,-56 531,-56 531,-62 525,-68 519,-68\"/>\r\n",
       "<text text-anchor=\"start\" x=\"450\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"439.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\r\n",
       "<text text-anchor=\"start\" x=\"438.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 3]</text>\r\n",
       "<text text-anchor=\"start\" x=\"435\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = label1</text>\r\n",
       "</g>\r\n",
       "<!-- 9&#45;&gt;11 -->\r\n",
       "<g id=\"edge11\" class=\"edge\"><title>9&#45;&gt;11</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M440.714,-103.726C445.541,-95.0615 450.647,-85.8962 455.502,-77.1802\"/>\r\n",
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       "</g>\r\n",
       "</g>\r\n",
       "</svg>\r\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x1ff79a13cc0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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qpWml2WQg0zNdk5e43Zd2u16JpN9LCLfnBOX7RURqpGbE7Y7xtIt25SdGhZeS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\nDIBhGMaY8v8Dgv2Yvl4mQ0gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ff7cb5cba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 获取数据值所在的范围\n",
    "x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1\n",
    "y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1\n",
    "\n",
    "# 生成网格矩阵\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),\n",
    "                     np.arange(y_min, y_max, 0.02))\n",
    "\n",
    "z = model.predict(np.c_[xx.ravel(), yy.ravel()])# ravel与flatten类似，多维数据转一维。flatten不会改变原始数据，ravel会改变原始数据\n",
    "z = z.reshape(xx.shape)\n",
    "# 等高线图\n",
    "cs = plt.contourf(xx, yy, z)\n",
    "# 样本散点图\n",
    "plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      1.00      1.00        47\n",
      "        1.0       1.00      1.00      1.00        53\n",
      "\n",
      "avg / total       1.00      1.00      1.00       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(x_data)\n",
    "print(classification_report(predictions,y_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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